Pneumonia Detection On Chest X-Ray Using Radiomic Features And Contrastive Learning

By Yan Han, Chongyan Chen, Ahmed H. Tewfik, Ying Ding. 2021

48 citations recorded by the source corpus.

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still has been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models ($\gt 10$ % in F1-score) and increases the model’s interpretability.

Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Contrastive Learning of Medical Visual Representations from Paired Images and Text (2020, background): “Moreover, a similar idea can be extended to include other modalities of healthcare data, including multiomics data (Han et al., 2021) or patient metadata (Vu et al., 2021), for more robust and cost-eﬀective machine learning applications in the healthcare domain.”
Self-supervised learning in medicine and healthcare (2022, unknown): “This methodology has also shown its potential in work that has applied a version of contrastive learning to chest X-ray diagnosis 9,11–13 .”
Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning (2022, methodology): “For instance, the self-supervised method could leverage the availability of pathology reports that describe diagnoses such as cancer present in histopathology scans 26,35,36 .”